Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR

Abstract

Transmission lines are the basis of human production and activities. In order to ensure their safe operation, it is essential to regularly conduct transmission line inspections and identify tree risk in a timely manner. In this paper, a power line extraction and tree risk detection method is proposed. Firstly, the height difference and local dimension feature probability model are used to extract power line points, and then the Cloth Simulation Filter algorithm and neighborhood sharing method are creatively introduced to distinguish conductors and ground wires. Secondly, conductor reconstruction is realized by the approach of the linear–catenary model, and numerous non-risk points are excluded by constructing the tree risk point candidate area centered on the conductor’s reconstruction curve. Finally, the grading strategy for the safety distance calculation is used to detect the tree risk points. The experimental results show that the precision, recall, and F-score of the conductors (ground wires) classification exceed 98.05% (97.98%), 99.00% (99.14%), and 98.58% (98.56%), respectively, which presents a high classification accuracy. The Root-Mean-Square Error, Maximum Error, and Minimum Error of the conductor’s reconstruction are better than 3.67 cm, 7.13 cm, and 2.64 cm, respectively, and the Mean Absolute Error of the safety distance calculation is better than 6.47 cm, proving the effectiveness and rationality of the proposed tree risk points detection method.